• International Journal of Highway Engineering
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• v.4 no.3 s.13
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• pp.1-13
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• 2002

Design methods for new flexible pavements and overlays are in the transition from empirical to mechanistic approach, and many state highway agencies trend to move toward the adoption and use of mechanistic-empirical (M-E) design in new constructions and rehabilitations of flexible pavements. Hence, the Michigan Department of Transportation (MDOT) decided to develop a M-E flexible pavement design procedure, in which major pavement distresses such as fatigue cracking and rutting are employed as indicators of the serviceability of a flexible pavement. The main concept of the developed design procedure is that a designed pavement that is supposed to carry a certain number of traffic must satisfy designated thresholds of rut depths and fatigue lives during a service period. For the M-E design procedure, transfer functions were developed to predict rut-depths and fatigue lives. These functions related the pavement responses to pavement performance. For validation, three current new flexible pavement design cases were obtained from the MDOT. In these cases, asphalt concrete (AC) layer thicknesses determined by the suggested M-E procedure compare favorably with those determined by the current MDOT design practice that is based on AASHTO design guide. This finding implies that the suggested Michigan M-E flexible pavement design procedure can provide a good opportunity to improve the current design practice.

• Journal of Korean Society of Transportation
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• v.35 no.5
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• pp.409-422
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• 2017

TOD (Transit-Oriented Development) is one of the urban structure concentrated on the multifunctional space/district with public transportation system, which is introduced for maintaining sustainable future cities. With such trends, the project of building complex transferring centers located at a urban railway station has widely been spreaded and a comprehensive and systematic analytical framework is required to clarify and readily understand the complicated procedure of estimation with the large scale of the project. By doing so, this study is to develop a comprehensive analytical framework for estimating a pedestrian OD matrix using a spatial information and an integrated smart card data, which is so called a data depository and it has been applied to the Samseong station for the model validation. The proposed analytical framework contributes on providing a chance to possibly extend with digitalized and automated data collection technologies and a BigData mining methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.137-141
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• 2007

우리나라는 지역적 특성상 여름철에 강수량이 편중되어 있어 계절별로 하천유량의 편차가 매우 크게 나타나고 있다. 또한 계속되는 도시화와 산업화로 인하여 하천에서 다양한 형태의 취수와 이용이 이루어지고, 국민들의 수질향상 등에 대한 기대가 커짐에 따라 하천유량에 대한 양적 질적 요구가 증대되고 있다. 이에 효율적인 수자원운영을 위하여 다양한 형태의 수자원확보가 이루어지고 있으며, 이중 가장 대표적인 댐을 이용하여 운영에 따른 하류 하천에서의 유황개선효과에 대해서 유역 장기분포모의 모형 중 하나인 SWAT모형을 밀양강유역에 적용하여 일 유출량 모의를 수행하였다. 모형의 구축은 $2000{\sim}2005$년의 밀양강유역 기상자료를 활용하였으며, 건교부에서 운영하는 Wamis를 이용하여 DEM, 토지이용도, 토양도를 취득하였다. 또한 밀양댐, 운문댐 유입량과 밀양2지점의 측정유량을 이용하여, SWAT의 다양한 매개변수 중 SOL_AWC, ESCO, CH_K등을 수정하여 Calibration을 실시하였으며, 밀양강유역 오염총량지점인 밀양A지점과 밀양B지점의 측정유량을 이용하여 Validation을 실시하였다. 그 결과, 댐의 경우 0.9이상의 선형상관관계를 나타내었으며, 오염총량지점의 경우 0.6이상의 선형상관관계를 나타내어 모형의 적용성이 양호함을 알 수 있었다. 과거 39년간의 강수량자료를 이용, 구축된 모형의 유역모의운영을 통해 밀양강 유역의 밀양댐과 운문댐이 있음으로 해서, 댐이 없는 경우와 비교, 하류 지점별 유황개선에 어느 정도 효과가 있는지 댐의 순기능에 대한 정량적인 분석을 수행하였다. 또한 댐별 방류량을 변동하여 하류 주요지점에 미치는 유황개선효과를 정량화하였다. 마지막으로 댐의 효율을 최대화한 하류확보가능하천유지유량을 월별평균량으로 산정하였다. 이는 향후 오염총량제 기준유량 및 환경용수의 법제화를 통한 하천유지용수의 증가시 비구조적 대책의 공급가능 최대량으로 활용가능할 것으로 사료된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• Korean Journal of Ecology and Environment
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• v.46 no.1
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• pp.1-9
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• 2013

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

• Particle and aerosol research
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• v.5 no.3
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• pp.139-148
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• 2009

In Seoul, the largest emission source for volatile organic compounds (VOCs) based on the emission inventory is solvent usage followed by vehicular exhaust. However, according to a CMB modeling result by Na and Kim (2007), vehicular exhaust was the largest emission source followed by solvent usage. Detailed analyses on the validity of the CMB model result were carried out and it was suggested that the existing emission inventory for VOCs might be underestimating vehicular emission. Scientific considerations that should be considered for the effective control strategy against VOCs are discussed.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.7
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• pp.907-911
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• 2017

Objective: Intramuscular fat is one of the meat quality traits that is considered in the selection strategies for Hanwoo (Korean cattle). Different methods are used to estimate the breeding value of selection candidates. In the present work we focused on accuracy of different genotype relationship matrices as described by forni and pedigree based relationship matrix. Methods: The data set included a total of 778 animals that were genotyped for BovineSNP50 BeadChip. Among these 778 animals, 72 animals were sires for 706 reference animals and were used as a validation dataset. Single trait animal model (best linear unbiased prediction and genomic best linear unbiased prediction) was used to estimate the breeding values from genomic and pedigree information. Results: The diagonal elements for the pedigree based coefficients were slightly higher for the genomic relationship matrices (GRM) based coefficients while off diagonal elements were considerably low for GRM based coefficients. The accuracy of breeding value for the pedigree based relationship matrix (A) was 13% while for GRM (GOF, G05, and Yang) it was 0.37, 0.45, and 0.38, respectively. Conclusion: Accuracy of GRM was 1.5 times higher than A in this study. Therefore, genomic information will be more beneficial than pedigree information in the Hanwoo breeding program.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.9-21
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• 2020

With the proliferation of RDF knowledge graphs(KGs), there arose a need of a standardized schema representation of the graph model for effective data interchangeability and interoperability. The need resulted in the development of SHACL specification to describe and validate RDF graph's structure by W3C. Relational databases(RDBs) are one of major sources for acquiring structured knowledge. The standard for automatic generation of RDF KGs from RDBs is R2RML, which is also developed by W3C. Since R2RML is designed to generate only RDF data graphs from RDBs, additional manual tasks are required to create the schemas for the graphs. In this paper we propose an approach to automatically generate SHACL schemas for RDF KGs populated by R2RML mappings. The key of our approach is that the SHACL shemas are built only from R2RML documents. We describe an implementation of our appraoch. Then, we show the validity of our approach with R2RML test cases designed by W3C.

• Journal of Medicine and Life Science
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• v.17 no.1
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• pp.7-15
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• 2020

The field triage guidelines have been widely implemented in the Korean trauma system. This study aimed to evaluate and validate whether it is reliable to use the field triage guidelines for predicting severe traumatic brain injury (TBI) and traumatic spinal injury (TSI) patients. This study retrospectively analyzed in-hospital cohort registries of all TBI and TSI patients, who visited the emergency department (ED) of the Jeju National University Hospital from 1 January 2013 to 31 December 2015. The primary outcome was defined as TBI and TSI patients with an injury severity score (ISS)>15. Secondary outcomes were defined as cases in which one or more of the following conditions: in-hospital death, ISS>15, admission to the intensive care unit, emergency surgery. We enrolled 14,889 TBI and TSI patients who visited ED, of which 7,966 (53.5%) were triage positive. The overall sensitivity, specificity and area under the curve (AUC) of the full cumulative field triage guidelines step's model (Step 1+3+4 criteria) for primary outcome were 82.8%, 47.0%, and 0.646, respectively. In the results for secondary outcomes, the specificity did not show a significant difference, but the sensitivity decreased to 66.5% and AUC to 0.568. The results of this study suggest that further optimization of the field triage guidelines is needed to identify high-risk TBI and TSI patients.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.29-41
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• 2006

Geostatistical data are obtained only at selected sites even though they are potentially available at any location In a continuous surface. Therefore it is necessary to estimate the unknown values at unsampled locations based on observations. In this study we compared the accuracy of 5 spatial interpolation methods: local trend surface, IDW, RBF, ordinary kriging, universal kriging. These interpolation methods were applied to annual rainfall data. As the results of validation tests, universal kriging with gaussian variogram model showed the best accuracy in comparison with other interpolation methods. In the case of kriging, the predicted values were more accurate and within a more narrow range than other methods. In contrast with kriging, local trend surface analysis, IDW and RBF showed the wide range of predicted values and abrupt changes between neighbors.